Drexel University

College of Computing and Informatics

Temporal Smoothing in Sparse Coding

Research
Exploring temporally smooth representations via sparse coding and its biological plausibility in the human brain's visual system.
We consider the relationship between representations of natural images in a temporally smooth sequence (i.e. consecutive frames in a video). Traditionally, sparse coding methods learn representations of images in isolation. Here, we learn an image's sparse representation with the previous image's representation as a starting point. Our investigation links neuroscience and representation learning and builds on the increasingly popular field of machine learning, specifically neuro-biologically inspired learning models.
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Team Members

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Behind The Scenes

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